A framework for creating impactful autonomous driving datasets is presented, starting with gap diagnosis between data and evaluation problems and selecting minimal operators, demonstrated with the KITScenes dataset.
The ApolloScape open dataset for autonomous driving and its application
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Clear2Fog simulates fog on 270k Waymo images; mixed-density fog at 75% scale matches full fixed-density training performance, and adjusted learning rates improve sim-to-real transfer by up to 1.17 mAP.
citing papers explorer
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Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark
A framework for creating impactful autonomous driving datasets is presented, starting with gap diagnosis between data and evaluation problems and selecting minimal operators, demonstrated with the KITScenes dataset.
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A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline
Clear2Fog simulates fog on 270k Waymo images; mixed-density fog at 75% scale matches full fixed-density training performance, and adjusted learning rates improve sim-to-real transfer by up to 1.17 mAP.